import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class GCN(torch.nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        torch.manual_seed(1234)
        self.conv = GCNConv(in_channels, out_channels, add_self_loops=False)

    def forward(self, x, edge_index, edge_weight=None):
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.conv(x, edge_index, edge_weight).relu()
        return x
    

class GCN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super(GCN, self).__init__()
        torch.manual_seed(42)

        # Initialize the layers
        self.conv1 = GCNConv(dataset.num_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)
        self.out = Linear(hidden_channels, dataset.num_classes)

    def forward(self, x, edge_index):
        # First Message Passing Layer (Transformation)
        x = self.conv1(x, edge_index)
        x = x.relu()
        x = F.dropout(x, p=0.5, training=self.training)

        # Second Message Passing Layer
        x = self.conv2(x, edge_index)
        x = x.relu()
        x = F.dropout(x, p=0.5, training=self.training)

        # Output layer 
        x = F.softmax(self.out(x), dim=1)
        return x
    


# address: https://www.kaggle.com/code/pinocookie/pytorch-simple-mlp/notebook

class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        self.layers = nn.Sequential(
            nn.Linear(784, 100),
            nn.ReLU(),
            nn.Linear(100, 10)
        )
        
    def forward(self, x):
        # convert tensor (128, 1, 28, 28) --> (128, 1*28*28)
        x = x.view(x.size(0), -1)
        x = self.layers(x)
        return x




# model = GCN(dataset.num_features, dataset.num_classes)
# model.train()
# optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# print("Training on CPU.")

# for epoch in range(1, 6):
#     optimizer.zero_grad()
#     out = model(data.x, data.edge_index, data.edge_attr)
#     loss = F.cross_entropy(out, data.y)
#     loss.backward()
#     optimizer.step()
#     print(f"Epoch: {epoch}, Loss: {loss}")